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Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades

Author

Listed:
  • Yi Yang

    (Department of Civil Engineering, Aalborg University, 9220 Aalborg, Denmark)

  • John Dalsgaard Sørensen

    (Department of Civil Engineering, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Due to the considerable increase in clean energy demand, there is a significant trend of increased wind turbine sizes, resulting in much higher loads on the blades. The high loads can cause significant out-of-plane deformations of the blades, especially in the area nearby the maximum chord. This paper briefly presents a discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades, based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the aforementioned challenges facing wind turbine blades. The in-history inspection information extracted from a database with inspection information was used to calibrate transition probabilities in the discrete Markov chain model. With the calibrated transition probabilities, the damage evolution can be statistically simulated. The classical Bayesian pre-posterior decision theory, as well as condition-based maintenance strategy, was used as a basis for the decision-making. An illustrative example with transverse cracks is presented using a reference wind turbine.

Suggested Citation

  • Yi Yang & John Dalsgaard Sørensen, 2019. "Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades," Energies, MDPI, vol. 12(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:998-:d:213938
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    References listed on IDEAS

    as
    1. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    2. Yuan, X.-X. & Pandey, M.D., 2009. "A nonlinear mixed-effects model for degradation data obtained from in-service inspections," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 509-519.
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    4. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    5. Cheng, Tianjin & Pandey, Mahesh D. & van der Weide, J.A.M., 2012. "The probability distribution of maintenance cost of a system affected by the gamma process of degradation: Finite time solution," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 65-76.
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    Cited by:

    1. Jyh-Cherng Gu & Chun-Hung Liu & Kai-Ying Chou & Ming-Ta Yang, 2019. "Research on CBM of the Intelligent Substation SCADA System," Energies, MDPI, vol. 12(20), pages 1-22, October.
    2. Martyna Tomala & Andrzej Rusin & Adam Wojaczek, 2020. "Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility," Energies, MDPI, vol. 13(13), pages 1-16, July.
    3. Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    4. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).

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